본고에서는 한자라는 계열에서 서체라는 개념을 어떻게 분석에 이용하면 좋을지를 이시즈카 하루미치(石塚晴通)의 한자자체론(漢字字體論)을 중심으로 검토하였다. 이시즈카의 한자자체 론은 서체를 글자를 아우르는 표준으로서 상위에 둔다. 이것은 자료 연구의 장에서 탄생한 실지 적인 것임을 학설의 발전 과정을 통해 알 수 있다. 또한 이시즈카가 일컫는 서체는 실질적으로는 문자 체계로서의 차이에 가까운 측면이 있으며, 오늘날 동일한 글자에 대한 서기 방식의 차이로 인식하는 것과는 이질적이라는 점을 지적하고, 그러한 현대적인 감각에 기인하는 오독 사례를 지적하였다. 그런 다음 역사적인 서체와 인쇄 서체를 구별하여 논의할 필요가 있다는 점, 이시즈 카 한자자체론을 화용론적 문헌학 pragmaphilology (Smith, 2020)의 방법으로 자리매김할 수 있음을 논하였다.

Bellows expansion joints enhance the displacement performance of piping systems owing to their unique geometrical features. However, structural uncertainties such as wall thinning in convolutions, a byproduct of the manufacturing process, can impair their structural integrity. This study addresses such issues by conducting a global sensitivity analysis to assess the impact of these uncertainties on the performance of bellows expansion joints under monotonic loading. Global sensitivity analysis, which examines main and nth order interaction effects, is computationally expensive. To mitigate this, we employed a surrogate model-based approach using an artificial neural network. This model demonstrated robust prediction capabilities, as evidenced by metrics such as the coefficient of determination. The sensitivity indices of the main effect for the 2-ply and 3-ply bellows at the sixth convolution were 0.3340 and 0.3233, respectively. The sensitivity index of the sixth convolution was larger than that of other convolutions because the maximum deformation of the bellows expansion joint under monotonic bending load occurs around it. Interestingly, the sensitivity index for the interaction effect was negligible (0.01%) compared to the main effect, suggesting minimal activity between uncertainty factors across convolutions. Notably, bellows expansion joints under repetitive loading exhibit more complex behaviors, with the initial leakage typically occurring at the convolution. Therefore, future studies should focus on the structural uncertainties of bellows expansion joints under cyclic loading and employ a surrogate model for comprehensive global sensitivity analysis.

이 연구는 초분광 영상으로 두 품종의 콩(청자 3호, 대찬)의 들불병을 진단할 수 있는 모델과 다중분광 영상센서를 개발하기 위해 수행되었다. 무처리구와 들불병 처리구에서 5 nm full width at half maximum (FWHM)으로 구성된 원시 초분광 중심파장들의 콩 식물 영역 반사율들을 추출하여 10 nm FWHM으로 병합한 후, t-test로 차이가 나타난 blue, green, red, red edge, NIR1 및 NIR2 각 영역에서 선정된 대표 밴드로 121개의 식생지수를 계산하였다. 식생지수를 입력변수로 support vector machine (SVM), random forest (RF), extra tree (EXT), extreme gradient boosting (XGB)의 머신러닝 기법과 shapley additive explanation 변수 선택 기법을 적용하여 들불병 진단에 가장 적절한 모델을 선정하고 사용된 식생지수와 파라미터를 나타내었다. T-test 결과 품종에 상관없이 blue 1개(420 nm), green 2개(500, 540 nm), red 1개(600 nm), red edge 2개(680, 700 nm), NIR1 2개(780, 840 nm), NIR2 1개(920 nm)의 총 9개 대표 밴드들이 선택되었고, 성능 평가를 통해 선정된 모델에 청자 3호의 경우 SVM모델(OA=0.86, KC=0.72, 10 VIs)이 선정되었으나 혼동행렬 분석 결과 정상오분류가 적은 RF모델이 선택되었다. RF모델(식 생지수 : RE/Blue, NSI, GDVI, Green/Blue, 파라미터 : max_depth=6, n_estimators=100)은 OA=0.81, KC=0.60, precision=0.86, recall=0.81, F1 score=0.80의 성능을 나타내었다. 대찬은 EXT모델(식생지수 : YVI, RE/Green, 2YVI, 파라미터 : max_depth=8, n_estimators=10)이 선정되 었고, OA=0.86, KC=0.72, precision=0.86, recall=0.86, F1 score=0.86의 성능을 나타내었다.

This study reports an analytical investigation on the development of SB4-grade separated concrete median barriers. The proposed barrier sections comprise three designs, with heights of 810, 1000, and 1270 mm and upper widths of 90, 120, and 120 mm, respectively. Before conducting collision analyses on the proposed sections, the considered collision analysis model was validated using real collision test results; the model was found to accurately predict the real collision test results. The proposed cross-sections were modeled to perform collision analysis according to SB4-grade collision conditions. Results indicated that increasing the cross-section height increased the damaged area and decreased the strength, while the occupant protection performance remained mostly unaffected. Furthermore, the proposed cross-sections met the strength and occupant protection performance criteria specified in domestic guidelines, suggesting their suitability as a separated concrete median barrier for bridges.

This study reports an experimental and analytical exploration of concrete columns laterally confined with Fe-based shape-memory alloy (Fe-SMA) spirals. For performing experiments, Fe-SMA rebars with a 4% prestrain and diameter of 10 mm were fabricated and concrete columns with internal Fe-SMA spiral reinforcement were constructed with a diameter of 200 mm and height of 600 mm. An acrylic bar with an attached strain gauge was embedded in the center of the specimen to measure local strains. Experimental variables encompassed the Fe-SMA spiral reinforcement, spacing, and activation temperature. Uniaxial compression tests were conducted after applying active confinement to the concrete columns through electrical-resistance heating. Notably, as the Fe-SMA spiral spacing decreased, the local failure zone length and compressive fracture energy of the prepared specimens increased. Additionally, a model incorporating compressive fracture energy was proposed to predict the stress–strain behavior of the. This model, accounting for active and passive confinement effects, demonstrated accurate predictions for the experimental results of this study as well as for previously reported results.

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

In this study, we developed Rapid Enrichment Broth for Vibrio parahaemolyticus (REB-V), a broth capable enriching V. parahaemolyticus from 100 CFU/mL to 106 CFU/mL within 6 hours, which greatly facilitates the rapid detection of V. parahaemolyticus. Using a modified Gompertz model and response surface methodology, we optimized supplement sources to rapidly enrich V. parahaemolyticus. The addition of 0.003 g/10 mL of D-(+)- mannose, 0.002 g/10 mL of L-valine, and 0.002 g/10 mL of magnesium sulfate to 2% (w/v) NaCl BPW was the most effective combination of V. parahaemolyticus enrichment. Optimal V. parahaemolyticus culture conditions using REB-V were at pH 7.84 and 37oC. To confirm REB-V culture efficiency compared to 2% (w/v) NaCl BPW, we assessed the amount of enrichment achieved in 7 hours in each medium and extracted DNA samples from each culture every hour. Real-time PCR was performed using the extracted DNA to verify the applicability of this REB-V culture method to molecular diagnosis. V. parahaemolyticus was enriched to 5.452±0.151 Log CFU/mL in 2% (w/v) NaCl BPW in 7 hours, while in REB-V, it reached 7.831±0.323 Log CFU/mL. This confirmed that REB-V enriched V. parahaemolyticus to more than 106 CFU/mL within 6 hours. The enrichment rate of REB-V was faster than that of 2% (w/v) NaCl BPW, and the amount of enrichment within the same time was greater than that of 2% (w/v) NaCl BPW, indicating that REB-V exhibits excellent enrichment efficiency.

Velvet antler is widely used as a traditional medicine, and numerous studies have demonstrated its tremendous nutritional and medicinal values including immunity-enhancing effects. This study aimed to investigate different deer velvet extracts (Sample 1: raw extract, Sample 2: dried extract, and Sample 3: freeze-dried extract) for proximate composition, uronic acid, sulfated glycosaminoglycan, sialic acid, collagen levels, and chemical components using ultra-performance liquid chromatography-quadrupole-time-of-light mass spectrometry. In addition, we evaluated the cytotoxic effect of the deer velvet extracts on BV2 microglia, HT22 hippocampal cells, HaCaT keratinocytes, and RAW264.7 macrophages using the cell viability MTT assay. Furthermore, we evaluated acute toxicity of the deer velvet extracts at different doses (0, 500, 1000, and 2000 mg/kg) administered orally to both male and female ICR mice for 14 d (five mice per group). After treatment, we evaluated general toxicity, survival rate, body weight changes, mortality, clinical signs, and necropsy findings in the experimental mice based on OECD guidelines. The results suggested that in vitro treatment with the evaluated extracts had no cytotoxic effect in HaCaT keratinocytes cells, whereas Sample-2 had a cytotoxic effect at 500 and 1000 μg/mL on HT22 hippocampal cells and RAW264.7 macrophages. Sample 3 was also cytotoxic at concentrations of 500 and 1000 μg/mL to RAW264.7 and BV2 microglial cells. However, the mice treated in vivo with the velvet extracts at doses of 500–2000 mg/kg BW showed no clinical signs, mortality, or necropsy findings, indicating that the LD50 is higher than this dosage. These findings indicate that there were no toxicological abnormalities connected with the deer velvet extract treatment in mice. However, further human and animal studies are needed before sufficient safety information is available to justify its use in humans.

In contemporary global warfare, the significance and imperative of air transportation have been steadily growing. Nevertheless, the Korean Air Force currently operates only with small and medium-sized military cargo planes, lacking larger aircraft. Consequently, the efficiency of their operations is constrained by the limited air transport capacity and the aging of their existing fleet, among other factors. Therefore, we have to consider to make future air transportation capability. Although the 2nd large-sized cargo-plane acquisition project is ongoing, its quantity is very small. In this study, we propose an optimal prediction model that takes into account practical constraints such as parking space availability, pilot availability, wartime daily maximum loads, while simultaneously maximizing both the effectiveness and efficiency of transport capacity for future warfare envirionment.

Amphibious operations represent a pivotal military maneuver involving the transfer of landing forces via ships, boats, and aircraft from sea to land. The success of such operations can be the decisive factor in the outcome of a war. Nevertheless, planning an amphibious assault is an intricate and formidable task, demanding careful consideration of numerous variables. This complexity is particularly evident in the formulation of loading plans for troops and equipment onto naval vessels. Historical accounts underscore the profound repercussions of errors in planning and loading on the execution of these operations. In pursuit of efficient loading procedures characterized by precision and time-effectiveness, our study has delved into the realm of optimization modeling. Employing a mixed-integer mathematical programming approach, this optimization model offers a valuable tool to streamline and enhance the preparatory phase of amphibious operations.